Search

Sounds like “running water” and “buzzing bees” are classes of sounds which are a collective result of many similar acoustic events and are known as “sound textures”. Recent psychoacoustic study using sound textures by [1] reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the ‘cochlear envelope’. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures’ marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 66% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 90% of the variance in over 1000 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.

Background Cognitive models indicated that social anxiety disorder (SAD) would be caused and maintained by a biased attentional processing of threatening information. This study investigates whether socially anxious children may present impaired attentional engagement and disengagement from negative emotional faces, as well as their underlying event-related potential responses. Methods and findings Fifteen children with high levels of social anxiety (HSA; 9 boys; mean age = 9.99y; SD = 1.14) and twenty low socially anxious children (LSA; 16 boys; mean age = 10.47y; SD = 1.17) participated in a spatial cueing task in which they had to detect targets following neutral/disgusted faces in a valid or invalid location. No group effect was reported on reaction times [p>.05]. However, electrophysiological data showed lower P3a amplitude in HSA children compared with the LSA group when processing facial stimuli. They also reported larger N2 amplitudes for valid-disgusted targets and a larger P3a amplitude for the invalid-disgusted ones. Conclusion In terms of electrophysiological data, our results validated, the hypothesis of attentional disengagement difficulties in SAD children. We also confirm the idea that high levels of social anxiety are associated with cognitive control impairments and have a greater impact on the processing efficiency than on the performance effectiveness.

Background Early life stress (ELS) is an important risk factor for the development of depression. Impairments in reward learning and feedback sensitivity have been suggested. to be an intermediate phenotype in depression aetiology. We therefore hypothesised that healthy adults with a history of ELS would have impairments in reward learning and feedback sensitivity. Methods We recruited 64 adult participants with high levels of ELS and no diagnosis of a current mental health disorder in addition to 65 controls. Participants completed two online reward learning tasks: the probabilistic reversal learning task (PRLT) and probabilistic reward task (PRT). Participants also completed depression, anhedonia, social status and stress scales with PRLT data being additionally analysed utilising a reinforcement learning model. Results Participants with high levels of ELS showed decreased positive feedback sensitivity (PFS) in the PRLT compared to controls. High ELS participants also tended towards possessing a decreased model-free learning rate which strengthened in subsequent analysis. This was coupled with a decreased learning ability in the acquisition phase of block 1 following the practice session. Neither groups of participants showed a reward induced response bias in the PLT however high ELS participants exhibited decreased discrimination ability between stimuli; this was however accounted for by depression symptomology in further analysis. Conclusions These data suggest that healthy participants without a mental health diagnosis and high levels of ELS show deficits in PFS and reward learning in the PRLT that are distinct from depressed patients. These deficits may be relevant to an increased vulnerability to depression.

International audience; Numerous empirical and modeling studies have been done to find a relationship between postural stability and the susceptibility to motion sickness (MS). However, while the demonstration of a causal relationship between postural stability and the susceptibility to MS is still lacking, recent studies suggest that motion sick individuals have genuine deficits in selecting and reweighting multimodal sensory information. Here we investigate how the adaptation to changing postural situations develops and how the dynamics in multisensory integration is modulated on an individual basis along with MS susceptibility. We used a postural task in which participants stood on a posturographic platform with either eyes open (EO) or eyes closed (EC) during three minutes. The platform was static during the first minute (baseline phase), oscillated harmonically during the second minute (perturbation phase) and returned to its steady state for the third minute (return phase). Principal component (PC) analysis was applied to the sequence of short-term power density spectra of the antero-posterior position of the center of pressure. Results showed that the less motion-sick a participant is, the more similar is his balance between high and low frequencies for EO and EC conditions (as calculated from the eigenvector of the first PC). By fitting exponential decay models to the first PC score in the return phase, we estimated, for each participant in each condition, the sluggishness to return to the baseline spectrum. We showed that the de-adaptation following platform oscillation depends on the susceptibility to MS. These results suggest that non motion-sick participants finely adjust their spectrum in the perturbation phase (i.e. reweighting) and therefore take longer to return to their initial postural control particularly with eyes closed. Thus, people have idiosyncratic ways of doing sensory reweighting for postural control, these processes being tied to MS susceptibility.

This paper introduces anenhanced MSM(Mutual Subspace Method) methodology for gait recognition, to provide robustness to variations in walking speed. Theenhanced MSM (eMSM)methodology expands and adapts the MSM, commonly used for face recognition, which is a static/physiological biometric, to gait recognition, which is a dynamic/behavioral biometrics. To address the loss of accuracy during calculation of the covariance matrix in the PCA step of MSM, we use a 2D PCA-based mutual subspace. Furhtermore, to enhance the discrimination capability, we rotate images over a number of angles, which enables us to extract richer gait features to then be fused by a boosting method. The eMSM methodology is evaluated on existing data sets which provide variable walking speed, i.e. CASIA-C and OU-ISIR gait databases, and it is shown to outperform state-of-the art methods. While the enhancement to MSM discussed in this paper uses combinations of 2D-PCA, rotation, boosting, other combinations of operations may also be advantageous.

Pruritus is a common debilitating symptom experienced by hemodialysis patients. Treatment is difficult because the cause of uremic pruritus is not known. This study addressed the hypothesis that pruritus is caused by solutes that accumulate in the plasma when the kidneys fail. We sought to identify solutes responsible for uremic pruritus using metabolomic analysis to compare the plasma of hemodialysis patients with severe pruritus versus mild/no pruritus. Pruritus severity in hemodialysis patients was assessed using a 100-mm visual analogue scale (VAS), with severe pruritus defined as >70 mm and mild/no pruritus defined as urea, and serum levels of calcium, phosphorus, PTH, albumin, ferritin, and hemoglobin were similar in the Itch and No Itch patients. Metabolomic analysis identified 1,548 solutes of which 609 were classified as uremic. No difference in the plasma or plasma ultrafiltrate levels of any solute or group of solutes was found between the Itch and No Itch patients. Metabolomic analysis of hemodialysis patients did not reveal any solutes associated with pruritus. A limitation of metabolomic analysis is that the solute of interest may not be included in the metabolomic platform’s chemical library. A role for uremic solutes in pruritus remains to be established.

International audience; This study aims to highlight the relationships between the structure of smell compounds and their odors. For this purpose, heterogeneous data sources were screened, and 6038 odorant compounds and their known associated odors (162 odor notes) were compiled, each individual molecule being represented with a set of 1024 structural fingerprint. Several dimensional reduction techniques (PCA, MDS, t-SNE and UMAP) with two clustering methods (k-means and agglomerative hierarchical clustering AHC) were assessed based on the calculated fingerprints. The combination of UMAP with k-means and AHC methods allowed to obtain a good representativeness of odors by clusters, as well as the best visualization of the proximity of odorants on the basis of their molecular structures. The presence or absence of molecular substructures has been calculated on odorant in order to link chemical groups to odors. The results of this analysis bring out some associations for both the odor notes and the chemical structures of the molecules such as “woody” and “spicy” notes with allylic and bicyclic structures, “balsamic” notes with unsaturated rings, both “sulfurous” and “citrus” with aldehydes, alcohols, carboxylic acids, amines and sulfur compounds, and “oily”, “fatty” and “fruity” characterized by esters and with long carbon chains. Overall, the use of UMAP associated to clustering is a promising method to suggest hypotheses on the odorant structure-odor relationships.

Our own unique character traits make our behavior consistent and define our individuality. Yet, this consistency does not entail that we behave repetitively like machines. Like humans, animals also combine personality traits with spontaneity to produce adaptive behavior: consistent, but not fully predictable. Here, we study an iconically rigid behavioral trait – insect phototaxis – that that nevertheless also contains both components of individuality and spontaneity. In a light/dark T-maze, approximately 70% of a group of Drosophila fruit flies choose the bright arm of the T-Maze, while the remaining 30% walk into the dark. Taking the photopositive and the photonegative subgroups and re-testing them reveals the spontaneous component: a similar 70-30 distribution emerges in each of the two subgroups. Increasing the number of choices to ten choices, reveals the individuality component: flies with extremely negative first choices were more likely to show photonegative behavior in subsequent choices and vice versa. General behavioral traits, independent of light/dark preference, contributed to the development of this individuality. The interaction of individuality and spontaneity together explains why group averages, even for such seemingly stereotypical behaviors, are poor predictors of individual choices.

Facial appearance expresses numerous cues about physical qualities as well as psychosocial and personality traits. Attractive faces are recognized clearly when seen and are often viewed advantageously in professional, social and romantic relationships. On the other hand, self-perceived attractiveness is not well understood and has been mainly attributed to psychological and cognitive factors. Here we use 3-dimensional facial surface data of a large young adult population (n = 601) to thoroughly assess the effect of facial shape on self-perceived facial attractiveness. Our results show that facial shape had a measurable effect on self-perception of facial attractiveness in both sexes. In females, self-perceived facial attractiveness was linked to decreased facial width, fuller anterior part of the lower facial third and more pronounced middle forehead and root of the nose. Males favored a well-defined chin, flatter cheeks and zygomas, and more pronounced eyebrow ridges, nose and middle forehead. The findings of this study support the notion that self-perceived facial attractiveness is not only motivated by psychological traits, but objectively measured phenotypic traits also contribute significantly. The role of social stereotypes for facial attractiveness in modern society is also inferred and discussed.

International audience; Renewed consumer demand motivates the nutritional and sensory quality improvement of fruits and vegetables. Specialized metabolites being largely involved in nutritional and sensory quality of carrot, a better knowledge of their phenotypic variability is required. A metabolomic approach was used to evaluate phenotypic plasticity level of carrot commercial varieties, over three years and a wide range of cropping environments spread over several geographical areas in France. Seven groups of metabolites have been quantified by HPLC or GC methods: sugars, carotenoids, terpenes, phenolic compounds, phenylpropanoids and polyacetylenes. A large variation in root metabolic profiles was observed, in relation with environment, variety and variety by environment interaction effects in decreasing order of importance. Our results show a clear diversity structuration based on metabolite content. Polyacetylenes, beta-pinene and alpha-carotene were identified mostly as relatively stable varietal markers, exhibiting static stability. Nevertheless, environment effect was substantial for a large part of carrot metabolic profile and various levels of phenotypic plasticity were observed depending on metabolites and varieties. A strong difference of environmental sensitivity between varieties was observed for several compounds, particularly myristicin, 6MM and D-germacrene, known to be involved in responses to biotic and abiotic stress. This work provides useful information about plasticity in the perspective of carrot breeding and production. A balance between constitutive content and environmental sensitivity for key metabolites should be reached for quality improvement in carrot and other vegetables.

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.

Emotions and temperature are closely related through embodied processes, and people seem to associate temperature concepts with emotions. While this relationship is often evidenced by everyday language (e.g., cold and warm feelings), what remains missing to date is a systematic study that holistically analyzes how and why people associate specific temperatures with emotions. The present research aimed to investigate the associations between temperature concepts and emotion adjectives on both explicit and implicit levels. In Experiment 1, we evaluated explicit associations between twelve pairs of emotion adjectives derived from the circumplex model of affect, and five different temperature concepts ranging from 0°C to 40°C, based on responses from 403 native speakers of four different languages (English, Spanish, Japanese, Chinese). The results of Experiment 1 revealed that, across languages, the temperatures were associated with different regions of the circumplex model. The 0°C and 10°C were associated with negative-valanced, low-arousal emotions, while 20°C was associated with positive-valanced, low-to-medium-arousal emotions. Moreover, 30°C was associated with positive-valanced, high-arousal emotions; and 40°C was associated with high-arousal and either positive- or negative-valanced emotions. In Experiment 2 (N= 102), we explored whether these temperature-emotion associations were also present at the implicit level, by conducting Implicit Association Tests (IATs) with temperature words (coldandhot) and opposing pairs of emotional adjectives for each dimension of valence (Unhappy/Dissatisfiedvs.Happy/Satisfied) and arousal (Passive/Quietvs.Active/Alert) on native English speakers. The results of Experiment 2 revealed that participants held implicit associations between the word hot and positive-valanced and high-arousal emotions. Additionally, the word cold was associated with negative-valanced and low-arousal emotions. These findings provide evidence for the existence of temperature-emotion associations at both explicit and implicit levels across languages.

Introduction Empowerment is considered pivotal for how women access and use health care services and experience their sexual and reproductive rights. In Mozambique, women’s empowerment requires a better understanding and contextualization, including looking at factors that could drive empowerment in that context. This study aims to identify socioeconomic, demographic, and behavioural determinants of different domains of women’s empowerment in Mozambique. Methods Using the Demographic and Health Survey (DHS) conducted in 2015 for Mozambique, a sample of 2072 women aged between 15 and 49 years old were included in this study. The DHS’s indicators of women’s empowerment were used in a principal component analysis and the obtained components were identified as the domains of empowerment. Logistic regressions were run to estimate the association of socioeconomic, demographic, and behavioural characteristics with each domain of empowerment. Crude and adjusted odds ratios (OR) and respective 95% confidence intervals (95% CI) were calculated. Results Three domains of women’s empowerment were identified, namely (1) Beliefs about violence against women, (2) Decision-making, and (3) Control over sexuality and safe sex. Region, rurality, the experience of intimate partner violence (IPV) and partner’s controlling behaviours were associated with Beliefs about violence against women, while Decision-making and Control over sexuality and safe sex were also associated with education, age and wealth. Employment, polygamous marriage and religion was positively associated with Decision-making, and access to media increased the odds of Control over sexuality and safe sex. Conclusion Women’s empowerment seems to be determined by different socio-economic, demographic, and behavioural factors and this seems to be closely related to different domains of empowerment identified. This finding affirms the multi-dimensionality of empowerment as well as the importance of considering the context- and community-specific characteristics. We gratefully acknowledge the PhD grant from Fundação para a Ciência e a Tecnologia to SCL (SFRH/BD/146625/2019) and the contract to SF (info:eu-repo/grantAgreement/FCT/CEEC IND 2017/CEECIND/01516/2017/CP1406/CT0001/PT). We also thank the University of Cape Town, Faculty of Health Sciences for the Post Graduate Research Training Grant (FRC Award 2019) to SCL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Industrial robots have different capabilities and specifications according to the required applications. It is becoming difficult to select a suitable robot for specific applications and requirements due to the availability of several types with different specifications of robots in the market. Best-worst method is a useful, highly consistent and reliable method to derive weights of criteria and it is worthy to integrate it with the evaluation based on distance from average solution (EDAS) method that is more applicable and needs fewer number of calculations as compared to other methods. An example is presented to show the validity and usability of the proposed methodology. Comparison of ranking results matches with the well-known distance-based approach, technique for order preference by similarity to ideal solution and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods showing the robustness of the best-worst EDAS hybrid method. Sensitivity analysis performed using eighty to one ratio shows that the proposed hybrid MCDM methodology is more stable and reliable.

Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing meaningful peer groups, but peer groups must be compatible with business constraints such as size and stability considerations. Additionally, statistical peer groups are constructed from many different variables, and can be difficult to understand, especially for non-statistical audiences. We developed methodology to apply business constraints to clustering solutions and allow the decision-maker to choose the balance between statistical goodness-of-fit and conformity to business constraints. Several tools were utilised to identify complex distinguishing features in peer groups, and a number of visualisations are developed to explain high-dimensional clusters for non-statistical audiences. In a case study where peer group size was required to be small ($\leq 100$ members), we applied constrained clustering to a noisy high-dimensional data-set over two subsequent years, ensuring that the clusters were sufficiently stable between years. Our approach not only satisfied clustering constraints on the test data, but maintained an almost monotonic negative relationship between goodness-of-fit and stability between subsequent years. We demonstrated in the context of the case study how distinguishing features between clusters can be communicated clearly to different stakeholders with substantial and limited statistical knowledge., 22 pages, 4 figures

IntroductionThe hypothesis of a general psychopathology factor that underpins all common forms of mental disorders has been gaining momentum in contemporary clinical research and is known as thepfactor hypothesis. Recently, a semiotic, embodied, and psychoanalytic conceptualisation of thepfactor has been proposed called the Harmonium Model, which provides a computational account of such a construct. This research tested the core tenet of the Harmonium model, which is the idea that psychopathology can be conceptualised as due to poorly-modulable cognitive processes, and modelled the concept of Phase Space of Meaning (PSM) at the computational level.MethodTwo studies were performed, both based on a simulation design implementing a deep learning model, simulating a cognitive process: a classification task. The level of performance of the task was considered the simulated equivalent to the normality-psychopathology continuum, the dimensionality of the neural network’s internal computational dynamics being the simulated equivalent of the PSM’s dimensionality.ResultsThe neural networks’ level of performance was shown to be associated with the characteristics of the internal computational dynamics, assumed to be the simulated equivalent of poorly-modulable cognitive processes.DiscussionFindings supported the hypothesis. They showed that the neural network’s low performance was a matter of the combination of predicted characteristics of the neural networks’ internal computational dynamics. Implications, limitations, and further research directions are discussed.

Quantitative evaluation of piano performance is of interests in many fields, including music education and computational performance rendering. Previous studies utilized features extracted from audio or musical instrument digital interface (MIDI) files but did not address the difference between hands (DBH), which might be an important aspect of high-quality performance. Therefore, we investigated DBH as an important factor determining performance proficiency. To this end, 34 experts and 34 amateurs were recruited to play two excerpts on a Yamaha Disklavier. Each performance was recorded in MIDI, and handcrafted features were extracted separately for the right hand (RH) and left hand (LH). These were conventional MIDI features representing temporal and dynamic attributes of each note and computed as absolute values (e. g., MIDI velocity) or ratios between performance and corresponding scores (e. g., ratio of duration or inter-onset interval (IOI)). These note-based features were rearranged into additional features representing DBH by simple subtraction between features of both hands. Statistical analyses showed that DBH was more significant in experts than in amateurs across features. Regarding temporal features, experts pressed keys longer and faster with the RH than did amateurs. Regarding dynamic features, RH exhibited both greater values and a smoother change along melodic intonations in experts that in amateurs. Further experiments using principal component analysis (PCA) and support vector machine (SVM) verified that hand-difference features can successfully differentiate experts from amateurs according to performance proficiency. Moreover, existing note-based raw feature values (Basic features) and DBH features were tested repeatedly via 10-fold cross-validation, suggesting that adding DBH features to Basic features improved F1 scores to 93.6% (by 3.5%) over Basic features. Our results suggest that differently controlling both hands simultaneously is an important skill for pianists; therefore, DBH features should be considered in the quantitative evaluation of piano performance.

Facial expressions carry key information about an individual’s emotional state. Research into the perception of facial emotions typically employs static images of a small number of artificially posed expressions taken under tightly controlled experimental conditions. However, such approaches risk missing potentially important facial signals and within-person variability in expressions. The extent to which patterns of emotional variance in such images resemble more natural ambient facial expressions remains unclear. Here we advance a novel protocol for eliciting natural expressions from dynamic faces, using a dimension of emotional valence as a test case. Subjects were video recorded while delivering either positive or negative news to camera, but were not instructed to deliberately or artificially pose any specific expressions or actions. A PCA-based active appearance model was used to capture the key dimensions of facial variance across frames. Linear discriminant analysis distinguished facial change determined by the emotional valence of the message, and this also generalised across subjects. By sampling along the discriminant dimension, and back-projecting into the image space, we extracted a behaviourally interpretable dimension of emotional valence. This dimension highlighted changes commonly represented in traditional face stimuli such as variation in the internal features of the face, but also key postural changes that would typically be controlled away such as a dipping versus raising of the head posture from negative to positive valences. These results highlight the importance of natural patterns of facial behaviour in emotional expressions, and demonstrate the efficacy of using data-driven approaches to study the representation of these cues by the perceptual system. The protocol and model described here could be readily extended to other emotional and non-emotional dimensions of facial variance., Author summary Faces convey critical perceptual information about a person including cues to their identity, social traits, and their emotional state. To date, most research of facial emotions has used images of a small number of standardised facial expressions taken under tightly controlled conditions. However, such approaches risk missing potentially important facial signals and within-person variability in expressions. Here, we propose a novel protocol that allows the eliciting of emotional expressions under natural conditions, without requiring people to deliberately or artificially pose any specific facial expressions, by video recording people while they deliver statements of good or bad news. We use a model that captures the key dimensions of facial variability, and apply a machine learning algorithm to distinguish between the emotional expressions generated while giving good and bad news. By identifying samples along the discriminating dimension and projecting them back through the model into the image space, we can derive a behaviourally relevant dimension along which the faces appear to vary in emotional state. These results highlight the promise of data-driven techniques and the importance of employing natural images in the study of emotional facial expressions.

This article investigates acculturation stress among Pakistani students who are studying in Chinese universities, located in five provinces where international students are concentrated, with a mix-method approach. 203 students among 260 questionnaire recipients responded the online survey. When using the ASSIS (Acculturation Stress Scale for International Students) as instrument, the Principal Component Analysis Method and SPSS 20.0, we found that Pakistani students are under acculturative stress, 68.53%, 10.97% and 9.15% of them perceived discrimination, home sickness and perceived hate, and 5.25%, 3.11% and 2.58% of them fear, culture shock and guilt respectively. The qualitative segment of the study is consisted of 20 Pakistani students studying in 4 universities located in Wuhan city of Hubei capital enquiring through semi-structured interviews. The findings illustrate that Pakistani students in China are expressing their major concerns on culture shock, homesickness, food and language barriers while disconfirm ASSIS findings like perceived discrimination, hate, fear and guilt as factors responsible for acculturative stress. The study suggested that pre-departure orientation lectures about host country's cultural values and campus environment, and on-campus extra-curricular, cultural activities and maximum social interaction with local students can effectively acculturate students in new cultural setting, and can lower their acculturative stress.

Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20–77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during performance of a cognitive task. We used a simple brain-behavioral prediction technique based on Principal Component Analysis (PCA) and regression and studied the utility of both modalities in quasi out-of-sample predictions, using split-sample simulations (= 5-fold Monte Carlo cross validation) with 1,000 iterations for which a regression model predicting a cognitive outcome was estimated in a training sample, with a subsequent assessment of prediction success in a non-overlapping test sample. The sample assignments were identical for functional connectivity, voxel activation, and brain structure, enabling apples-to-apples comparisons of predictive utility. All 3 models that were investigated included the demographic covariates age, gender, and years of education. A minimal reference model using simple linear regression with just these 3 covariates was included for comparison as well and was evaluated with the same resampling scheme as described above. Results of the comparison between voxel activation and functional connectivity were mixed and showed some dependency on cognitive outcome; however, mean differences in predictive utility between voxel activation and functional connectivity were rather small in terms of within-modality variability or predictive success. More notably, only in the case of Fluid Reasoning did concurrent functional neuroimaging provided compelling about cognitive performance beyond structural brain imaging or the minimal reference model.

Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the characteristics of the coffee produced. The most efficient models choice to be applied should take into account the variety of information and the particularities of each biological material. This study was developed to evaluate statistical and machine learning models that would better discriminate environments through multi-traits of coffee genotypes and identify the main agronomic and beverage quality traits responsible for the variation of the environments. For that, 31 morpho-agronomic and post-harvest traits were evaluated, from field experiments installed in three municipalities in the Matas de Minas region, in the State of Minas Gerais, Brazil. Two types of post-harvest processing were evaluated: natural and pulped. The apparent error rate was estimated for each method. The Multilayer Perceptron and Radial Basis Function networks were able to discriminate the coffee samples in multi-environment more efficiently than the other methods, identifying differences in multi-traits responses according to the production sites and type of post-harvest processing. The local factors did not present specific traits that favored the severity of diseases and differentiated vegetative vigor. Sensory traits acidity and fragrance/aroma score also made little contribution to the discrimination process, indicating that acidity and fragrance/aroma are characteristic of coffee produced and all coffee samples evaluated are of the special type in the Mata of Minas region. The main traits responsible for the differentiation of production sites are plant height, fruit size, and bean production. The sensory trait "Body" is the main one to discriminate the form of post-harvest processing. Made available in DSpace on 2021-08-19T16:00:42Z (GMT). No. of bitstreams: 1 Machine-learning-and-statistics-to-qualify.pdf: 1998081 bytes, checksum: 5f8d40d907a02f2c08263f8c00f4cb1e (MD5) Previous issue date: 2021

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

Data Availability: All relevant data are within the manuscript and its Supporting Information files. Supporting information is available online at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233510#sec034 . Copyright: © 2020 Rosenblum et al. Walking stability is achieved by adjusting the medio-lateral and anterior-posterior dimensions of the base of support (step length and step width, respectively) to contain an extrapolated center of mass. We aimed to calculate total recovery time after different types of perturbations during walking, and use it to compare young and older adults following different types of perturbations. Walking trials were performed in 12 young (age 26.92 ± 3.40 years) and 12 older (age 66.83 ± 1.60 years) adults. Perturbations were introduced at different phases of the gait cycle, on both legs and in anterior-posterior or medio-lateral directions, in random order. A novel algorithm was developed to determine total recovery time values for regaining stable step length and step width parameters following the different perturbations, and compared between the two participant groups under low and high cognitive load conditions, using principal component analysis (PCA). We analyzed 829 perturbations each for step length and step width. The algorithm successfully estimated total recovery time in 91.07% of the runs. PCA and statistical comparisons showed significant differences in step length and step width recovery times between anterior-posterior and medio-lateral perturbations, but no age-related differences. Initial analyses demonstrated the feasibility of comparisons based on total recovery time calculated using our algorithm. The author MP funding for this work from the Israeli Ministry of Science and Technology - https://www.gov.il/en/Departments/Units/most_planning_and_control, grant #3-12072, and from the Israel Science Fund - https://www.isf.org.il/#/, grant #3-14527. The author UR is supported by a stipend from Ben-Gurion University of the Negev - https://in.bgu.ac.il/en/pages/default.aspx, as part of a PhD scholarship.

The animal-human relationship is essential for farm animal welfare and production. Generally, gentle tactile and vocal interactions improve the animal-human relationship in cattle. However, cows that are fearful of humans avoid their close presence and touch; thus, the animal-human relationship first has to be improved to a point where the animals accept stroking before their perception of the interactions and consequently the animal-human relationship can become positive. We tested whether the animal-human relationship of cows fearful of humans is improved more effectively by gentle interactions during restraint, allowing physical contact from the beginning, or if the gentle interactions are offered while the animals are free to move, giving them more control over the situation and thus probably a higher level of agency and a more positive perception of the interactions. Thirty-six dairy cows (median avoidance distance 1.6 m) were assigned to three treatments (each n = 12): gentle vocal and tactile interactions during restraint in the feeding rack (LOCK); gentle vocal and, if possible, tactile interactions while free in the barn (FREE); routine management without additional interactions (CON). Treatments were applied for 3 min per cow on 10 d per fortnight for 6 weeks (i.e., three periods). Avoidance and approach behaviour towards humans was tested before the start of the treatment period, and then at 2-week intervals. The recorded variables were reduced to one score by Principal Component Analysis. The resulting relationship score (higher values implying a better relationship with humans) increased in all groups; the increase was stronger in FREE than in CON, with the increase in LOCK being not significantly different from the other treatment groups. Thus, we recommend that gentle interactions with cows should take place while they are unrestrained, if possible.

The extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model, as well as object representations, are more widely distributed across the brain than previously acknowledged and that functional searchlight can improve model-based similarity and decoding accuracy. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation., Author summary There are two classical views about how the mind is organized in the brain. Early phrenology and neurophysiology and later neuropsychology argued that brain regions are specialized for certain functions of the mind. Older behavioral neuroscience and more recent neural network modeling and pattern classification instead argued against a one-to-one mapping, and rather that functions of the mind are distributed across multiple brain regions. Although there is considerable evidence for both perspectives in modern cognitive neuroscience, we hypothesize that the degree to which functions are distributed has been underestimated because of biases in prior work that favored finding specialized regions. Our novel machine learning approach, functional searchlight, reveals that features of a movie extracted with three different types of computational model and object representations are more widely distributed in the brain than suggested by current methods. Moreover, these distributed representations carry more movie content than could previously be decoded from the brain. This suggests a better way to conduct model-based analysis of brain data and provides a more solid basis on which to evaluate and refine theoretical models.

The uncertainty of high technology has determined the high-risk character of high-tech projects. Thus, it is of great importance to effectively avoid the risk of high-tech projects by thoroughly analyzing projects' methodologies and fully understanding the technology prospect risk of projects in the feasibility study phase. This study proposes a systematic research framework to identify and analyze the technology prospect risk of projects based on patent analysis. Thus, text mining technology and principal component analysis are used to improve the traditional patent map method and construct a technology prospect risk map. Moreover, patent value evaluation and correlation analysis methods are combined to identify technology potential areas and calculate the value of technology prospect risk. At the same time, an empirical study is conducted with project cases in the field of optical communications, and the technology prospect risk situations of these projects are ascertained through qualitative and quantitative methods. The study is innovative and practical and offers a better combination of analysis methods for current technology development and specific projects.

The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas defined by retinotopic mapping were examined using supervised classifiers applied to responses elicited by a range of stimuli. Using two distinct datasets obtained using wide-field and two-photon imaging, we show that the area labels predicted by the classifiers were highly consistent with the labels obtained using retinotopy. Furthermore, the classifiers were able to model the boundaries of visual areas using resting state cortical responses obtained without any overt stimulus, in both datasets. With the wide-field dataset, clustering neuronal responses using a constrained semi-supervised classifier showed graceful degradation of accuracy. The results suggest that responses from visual cortical areas can be classified effectively using data-driven models. These responses likely reflect unique circuits within each area that give rise to activity with stronger intra-areal than inter-areal correlations, and their responses to controlled visual stimuli across trials drive higher areal classification accuracy than resting state responses., Author summary The visual cortex has a prominent role in the processing of visual information by the brain. Previous work has segmented the mouse visual cortex into different areas based on the organization of retinotopic maps. Here, we collect responses of the visual cortex to various types of stimuli and ask if we could discover unique clusters from this dataset using machine learning methods. The retinotopy based area borders are used as ground truth to compare the performance of our clustering algorithms. We show our results on two datasets, one collected by the authors using wide-field imaging and another a publicly available dataset collected using two-photon imaging. The proposed supervised approach is able to predict the area labels accurately using neuronal responses to various visual stimuli. Following up on these results using visual stimuli, we hypothesized that each area of the mouse brain has unique responses that can be used to classify the area independently of stimuli. Experiments using resting state responses, without any overt stimulus, confirm this hypothesis. Such activity-based segmentation of the mouse visual cortex suggests that large-scale imaging combined with a machine learning algorithm may enable new insights into the functional organization of the visual cortex in mice and other species.

IntroductionLow Back Pain is a common public health problem worsened by maladaptive beliefs and incongruent back pain behaviour. It is imperative to develop outcome measures to assess these beliefs among patients with chronic LBP. This study aimed to cross-culturally adapt and determine the psychometric properties of the Yoruba version of the ODI (ODI-Y).MethodsThe ODI-Y was cross-culturally adapted following the process involving forward translation, synthesis, backward translation, expert review, and pilot testing. One hundred and thirty-six patients with chronic LBP took part in the validation of the ODI-Y; 86 of these individuals took part in the test-retest reliability (within 1-week interval) of the translated instrument. Internal consistency and test-retest reliability of the ODI-Y were determined using the Cronbach’s alpha and intra-class correlation. Other psychometric properties explored included the factor structure and fit, convergent validity, standard error of measurement and the minimal detectable change.ResultsThe mean age of the respondents was 50.5±10.6years. The ODI-Y showed a high internal consistency, with a Cronbach’s alpha (α) of 0.81. Test-retest of the Yoruba version of the ODI within 1-week interval yielded an Intra-Class Correlation coefficient of 0.89. The ODI-Y yielded a two-factor structure which accounted for 51.7% of the variance but showed poor fit. Convergent of ODI-Y with the visual analogue scale was moderate (r=0.30; p=0.00). The standard error of measurement and minimal detectable change of the ODI-Y were 2.0 and 5.5.ConclusionsThe ODI was adapted into the Yoruba language and proved to have a good factor structure and psychometric properties that replicated the results of other obtainable versions. We recommend it for use among Yoruba speaking patients with low-back pain.

Previous research shows that there is a correlation between attitudes and academic achievement. In this article, we analyze for the first time the impact of interactive groups (IG) and dialogic literary gatherings (DLG) on the attitudes that students show towards learning. A quantitative approach has been performed using attitude tests validated by previous research. The data suggest that in both cases, the participants show positive attitudes. The social context has an important influence on students' attitudes. The items with higher correlations include group work, mutual support, and distributed cognition. In the case of IGs, group work is much more appreciated, while in the case of DLGs, self-image and self-confidence are the two most clearly valued attitudes. The positive impact of IGs and DLGs on students' attitudes may have potential for teachers in transforming their practices and decision-making within the classroom.

The success of a scientist depends on their production of scientific papers and the impact factor of the journal in which they publish. Because most major scientific journals are published in English, success is related to publishing in this language. Currently, 98% of publications in science are written in English, including researchers from English as a Foreign Language (EFL) countries. Colombia is among the countries with the lowest English proficiency in the world. Thus, understanding the disadvantages that Colombians face in publishing is crucial to reducing global inequality in science. This paper quantifies the disadvantages that result from the language hegemony in scientific publishing by examining the additional costs that communicating in English creates in the production of articles. It was identified that more than 90% of the scientific articles published by Colombian researchers are in English, and that publishing in a second language creates additional financial costs to Colombian doctoral students and results in problems with reading comprehension, writing ease and time, and anxiety. Rejection or revision of their articles because of the English grammar was reported by 43.5% of the doctoral students, and 33% elected not to attend international conferences and meetings due to the mandatory use of English in oral presentations. Finally, among the translation/editing services reviewed, the cost per article is between one-quarter and one-half of a doctoral monthly salary in Colombia. Of particular note, we identified a positive correlation between English proficiency and higher socioeconomic origin of the researcher. Overall, this study exhibits the negative consequences of hegemony of English that preserves the global gap in science. Although having a common language is important for science communication, generating multilinguistic alternatives would promote diversity while conserving a communication channel. Such an effort should come from different actors and should not fall solely on EFL researchers.

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services’ QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics.

Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computational resources over time, and so might boost the performance of a physically finite brain or model. Here we show: (1) Recurrent convolutional neural network models outperform feedforward convolutional models matched in their number of parameters in large-scale visual recognition tasks on natural images. (2) Setting a confidence threshold, at which recurrent computations terminate and a decision is made, enables flexible trading of speed for accuracy. At a given confidence threshold, the model expends more time and energy on images that are harder to recognise, without requiring additional parameters for deeper computations. (3) The recurrent model’s reaction time for an image predicts the human reaction time for the same image better than several parameter-matched and state-of-the-art feedforward models. (4) Across confidence thresholds, the recurrent model emulates the behaviour of feedforward control models in that it achieves the same accuracy at approximately the same computational cost (mean number of floating-point operations). However, the recurrent model can be run longer (higher confidence threshold) and then outperforms parameter-matched feedforward comparison models. These results suggest that recurrent connectivity, a hallmark of biological visual systems, may be essential for understanding the accuracy, flexibility, and dynamics of human visual recognition., Author summary Deep neural networks provide the best current models of biological vision and achieve the highest performance in computer vision. Inspired by the primate brain, these models transform the image signals through a sequence of stages, leading to recognition. Unlike brains in which outputs of a given computation are fed back into the same computation, these models do not process signals recurrently. The ability to recycle limited neural resources by processing information recurrently could explain the accuracy and flexibility of biological visual systems, which computer vision systems cannot yet match. Here we report that recurrent processing can improve recognition performance compared to similarly complex feedforward networks. Recurrent processing also enabled models to behave more flexibly and trade off speed for accuracy. Like humans, the recurrent network models can compute longer when an object is hard to recognise, which boosts their accuracy. The model’s recognition times predicted human recognition times for the same images. The performance and flexibility of recurrent neural network models illustrates that modeling biological vision can help us improve computer vision.

Genetic data indicate differences in speciation rate across latitudes, but underlying causes have been difficult to assess because a critical phase of the speciation process is initiated in allopatry, in which, by definition, individuals from different taxa do not interact. We conducted song playback experiments between 109 related pairs of mostly allopatric bird species or subspecies in Amazonia and North America to compare the rate of evolution of male discrimination of songs. Relative to local controls, the number of flyovers and approach to the speaker were higher in Amazonia. We estimate that responses to songs of relatives are being lost about 6 times more slowly in Amazonia than in North America. The slow loss of response holds even after accounting for differences in song frequency and song length. Amazonian species with year-round territories are losing aggressive responses especially slowly. We suggest the presence of many species and extensive interspecific territoriality favors recognition of songs sung by sympatric heterospecifics, which results in a broader window of recognition and hence an ongoing response to novel similar songs. These aggressive responses should slow the establishment of sympatry between recently diverged forms. If male responses to novel allopatric taxa reflect female responses, then premating reproductive isolation is also evolving more slowly in Amazonia. The findings are consistent with previously demonstrated slower recent rates of expansion of sister taxa into sympatry, slower rates of evolution of traits important for premating isolation, and slower rates of speciation in general in Amazonia than in temperate North America., The use of more than 100 song playback experiments reveals that birds give stronger responses to songs of closely related species in the Amazon than in temperate North America, suggesting that speciation is currently happening more slowly in the species-rich tropics than in species-poor temperate regions.

The human brain tracks amplitude fluctuations of both speech and music, which reflects acoustic processing in addition to the encoding of higher-order features and one’s cognitive state. Comparing neural tracking of speech and music envelopes can elucidate stimulus-general mechanisms, but direct comparisons are confounded by differences in their envelope spectra. Here, we use a novel method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening. We expected to see music reconstruction match speech in a narrow range of frequencies, but instead we found that speech was reconstructed better than music for all frequencies we examined. Additionally, models trained on all stimulus types performed as well or better than the stimulus-specific models at higher modulation frequencies, suggesting a common neural mechanism for tracking speech and music. However, speech envelope tracking at low frequencies, below 1 Hz, was associated with increased weighting over parietal channels, which was not present for the other stimuli. Our results highlight the importance of low-frequency speech tracking and suggest an origin from speech-specific processing in the brain., Author summary The time-varying amplitude of sounds such as speech and music provides information about phrasing and rhythm, and previous research has shown that the brain continuously tracks these variations. Is a common neural mechanism responsible for tracking both sounds? We used a technique that reconstructs these amplitude fluctuations from the fluctuations in recorded EEG to quantify the strength of neural tracking. Our hypothesis was that neural tracking for music would match speech in a narrow frequency range associated with syllables and musical beats. Though our results did suggest a common mechanism involved in tracking both speech and music at these higher frequencies, we instead found that speech was tracked better than the rock, orchestral, and vocals stimuli at all of the frequencies we examined. Moreover, low-frequency fluctuations at the rate of phrases were associated with increased EEG weightings over parietal scalp, which did not appear during the neural tracking of music. These results suggest that, unlike syllable- and beat-tracking, phrase-level tracking of amplitude variations in speech may be speech-specific.

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

The Machiavellian IV [1] instrument, developed almost 50 years ago to measure trait Machiavellianism and still in wide use in personality research, uses item wording that is not gender-neutral, makes use of idiomatic expressions, and includes archaic references. In this two-sample study, exploratory factor analysis (EFA) was conducted on one sample to examine the structure of responses to the Mach IV. In an independent second sample the resulting EFA structure was analyzed using confirmatory factor analysis-based measurement equivalence/invariance (ME/I) tests in a control group with the original archaic items and a treatment group with eight items rewritten in a more modern vernacular. Specific model testing steps [2] and statistical tests [3] were applied in a bottom-up approach [4] to ME/I tests on these two versions of the Mach IV. The two versions were found to have equal form, equal factor loadings, but unequal indicator error variances. Subsequent item-by-item tests of error invariance resulted in substantial decrements to fit for three revised items suggesting that the error associated with these items was not equal across the two versions.

Few studies have examined the sustainability of family planning program outcomes in the post-program period. This article presents the results of a natural experiment where the Nigerian Urban Reproductive Health Initiative Phase I programming ended in early 2015 and Phase II activities continued in a subset of cities. Using data collected in 2015 and 2017, we compare contraceptive ideation and modern family planning use in two cities: Ilorin where program activities concluded in 2015 and Kaduna where program activities continued. The results demonstrate that exposure to program activities decreased in Ilorin but for those individuals reporting continuing exposure, the effect size of exposure on modern family planning use remained the same and was not significantly different from Kaduna. Modern family planning use continued to increase in both cites but at a lower rate than during Phase I. The results are useful for designing family planning programs that sustain beyond the life of the program.